Intelligent Singularity Avoidance in UR10 Robotic Arm Path Planning Using Hybrid Fuzzy Logic and Reinforcement Learning

TL;DR

This paper introduces a hybrid fuzzy logic and reinforcement learning system for UR10 robotic arm path planning that effectively detects and avoids singularities, improving safety and success rates in manipulation tasks.

Contribution

It presents a novel integrated approach combining fuzzy logic and reinforcement learning for real-time singularity avoidance in robotic path planning.

Findings

Achieved 90% success rate in reaching targets safely.

Demonstrated effective singularity detection using manipulability and condition number.

Validated system performance in simulation and real-world deployment.

Abstract

This paper presents a comprehensive approach to singularity detection and avoidance in UR10 robotic arm path planning through the integration of fuzzy logic safety systems and reinforcement learning algorithms. The proposed system addresses critical challenges in robotic manipulation where singularities can cause loss of control and potential equipment damage. Our hybrid approach combines real-time singularity detection using manipulability measures, condition number analysis, and fuzzy logic decision-making with a stable reinforcement learning framework for adaptive path planning. Experimental results demonstrate a 90% success rate in reaching target positions while maintaining safe distances from singular configurations. The system integrates PyBullet simulation for training data collection and URSim connectivity for real-world deployment.